English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Although temporal persistence, or permanence, is a well understoodrequirement for optimal biometric features, there is no general agreement onhow to assess temporal persistence. We suggest that the best way to assesstemporal persistence is to perform a test-retest study, and assess test-retestreliability. For ratio-scale features that are normally distributed, this isbest done using the Intraclass Correlation Coefficient (ICC). For 10 distinctdata sets (8 eye-movement related, and 2 gait related), we calculated thetest-retest reliability ('Temporal persistence') of each feature, and comparedbiometric performance of high-ICC features to lower ICC features, and to theset of all features. We demonstrate that using a subset of only high-ICCfeatures produced superior Rank-1-Identification Rate (Rank-1-IR) performancein 9 of 10 databases (p = 0.01, one-tailed). For Equal Error Rate (EER), usinga subset of only high-ICC features produced superior performance in 8 of 10databases (p = 0.055, one-tailed). In general, then, prescreening potentialbiometric features, and choosing only highly reliable features will yieldbetter performance than lower ICC features or than the set of all featurescombined. We hypothesize that this would likely be the case for any biometricmodality where the features can be expressed as quantitative values on aninterval or ratio scale, assuming an adequate number of relatively independentfeatures.

Method to Assess the Temporal Persistence of Potential Biometric Features: Application to Oculomotor, and Gait-Related Databases